# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains a factory for building various models."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from preprocessing import cifarnet_preprocessing
from preprocessing import inception_preprocessing
from preprocessing import lenet_preprocessing
from preprocessing import vgg_preprocessing

slim = tf.contrib.slim


# def get_preprocessing(name, is_training=False):
#   """Returns preprocessing_fn(image, height, width, **kwargs).

#   Args:
#     name: The name of the preprocessing function.
#     is_training: `True` if the model is being used for training and `False`
#       otherwise.

#   Returns:
#     preprocessing_fn: A function that preprocessing a single image (pre-batch).
#       It has the following signature:
#         image = preprocessing_fn(image, output_height, output_width, ...).

#   Raises:
#     ValueError: If Preprocessing `name` is not recognized.
#   """
#   preprocessing_fn_map = {
#       'cifarnet': cifarnet_preprocessing,
#       'inception': inception_preprocessing,
#       'inception_v1': inception_preprocessing,
#       'inception_v2': inception_preprocessing,
#       'inception_v3': inception_preprocessing,
#       'inception_v4': inception_preprocessing,
#       'inception_resnet_v2': inception_preprocessing,
#       'lenet': lenet_preprocessing,
#       'mobilenet_v1': inception_preprocessing,
#       'resnet_v1_50': vgg_preprocessing,
#       'resnet_v1_101': vgg_preprocessing,
#       'resnet_v1_152': vgg_preprocessing,
#       'resnet_v1_200': vgg_preprocessing,
#       'resnet_v2_50': vgg_preprocessing,
#       'resnet_v2_101': vgg_preprocessing,
#       'resnet_v2_152': vgg_preprocessing,
#       'resnet_v2_200': vgg_preprocessing,
#       'vgg': vgg_preprocessing,
#       'vgg_a': vgg_preprocessing,
#       'vgg_16': vgg_preprocessing,
#       'vgg_19': vgg_preprocessing,
#   }

#   if name not in preprocessing_fn_map:
#     raise ValueError('Preprocessing name [%s] was not recognized' % name)

#   def preprocessing_fn(image, output_height, output_width, **kwargs):
#     return preprocessing_fn_map[name].preprocess_image(
#         image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn
